SEARCH · bioRxiv Science

Results for “Biochemistry”

Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 811 records · Page 45Linked to original sources

bioRxiv2018

Proteome-wide solubility and thermal stability profiling reveals distinct regulatory roles for ATP

Nucleotide triphosphates (NTPs) regulate numerous biochemical processes in cells as (co-)substrates, allosteric modulators, biosynthetic precursors, and signaling molecules1-3. Apart from its roles as energy source fueling cellular biochemistry, adenosine triphosphate (ATP), the most abundant NTP in cells, has been reported to affect macromolecular assemblies, such as protein complexes4,5 and membrane-less organelles6-8. Moreover, both ATP and guanosine triphosphate (GTP) have recently been shown to dissolve protein aggregates9. However, system-wide studies to characterize NTP-interactions under conditions approximating the native cellular environment are lacking, which limits our perspective of the diverse physiological roles of NTPs. Here, we have mapped and quantified proteome-wide NTP-interactions by assessing thermal stability and solubility of proteins using mechanically disrupted cells. Our results reveal diverse biological roles of ATP depending on its concentration. We found that ATP specifically interacts with proteins that utilize it as substrate or allosteric modulator at doses lower than 500 M, while it affects protein-protein interactions of protein complexes at mildly higher concentrations (between 1-2 mM). At high concentrations (> 2 mM), ATP modulates the solubility state of a quarter of the insoluble proteome, consisting of positively charged, intrinsically disordered, nucleic acid binding proteins, which are part of membrane-less organelles. The extent of solubilization depends on the localization of proteins to different membrane-less organelles. Furthermore, we uncover that ATP regulates protein-DNA interactions of the Barrier to autointegration factor (BANF1). Our data provides the first quantitative proteome-wide map of ATP affecting protein structure and protein complex stability and solubility, providing unique clues on its role in protein phase transitions.

systems biology↗

bioRxiv2018

Prediction of a plant intracellular metabolite content class using image-based deep learning

Plant-derived secondary metabolites play a vital role in the food, pharmaceutical, agrochemical and cosmetic industry. Metabolite concentrations are measured after extraction, biochemistry and analyses, requiring time, access to expensive equipment, reagents and specialized skills. Additionally, metabolite concentration often varies widely among plants, even within a small area. A quick method to estimate the metabolite concentration class (high or low) will significantly help in selecting trees yielding high metabolites for the metabolite production process. Here, we demonstrate a deep learning approach to estimate the concentration class of an intracellular metabolite, azadirachtin, using models built with images of leaves and fruits collected from randomly selected Azadirachta indica (neem) trees in an area spanning >500,000 sqkms and their corresponding biochemically measured metabolite concentrations. We divided the input data randomly into training- and test-sets ten times to avoid sampling bias and to optimize the model parameters during cross-validation. The training-set contained >83,000 fruit and >86,000 leaf images. The best models yielded prediction errors of 19.13% and 15.11% (for fruit), and 8% and 26.67% (for leaf), each, for low and high metabolite classes, respectively. We further validated the fruit model using independently collected fruit images from different locations spanning nearly 130,000 sqkms, with 70% accuracy. We developed a desktop application to scan offline image(s) and a mobile application for real-time utility to predict the metabolite content class. Our work demonstrates the use of a deep learning method to estimate the concentration class of an intracellular metabolite using images, and has broad applications and utility.

bioinformatics↗

bioRxiv2018

Fast and accurate large multiple sequence alignments using root-to-leave regressive computation

Inferences derived from large multiple alignments of biological sequences are critical to many areas of biology, including evolution, genomics, biochemistry, and structural biology. However, the complexity of the alignment problem imposes the use of approximate solutions. The most common is the progressive algorithm, which starts by aligning the most similar sequences, incorporating the remaining ones following the order imposed by a guide-tree. We developed and validated on protein sequences a regressive algorithm that works the other way around, aligning first the most dissimilar sequences. Our algorithm produces more accurate alignments than non-regressive methods, especially on datasets larger than 10,000 sequences. By design, it can run any existing alignment method in linear time thus allowing the scale-up required for extremely large genomic analyses.\n\nOne Sentence SummaryInitiating alignments with the most dissimilar sequences allows slow and accurate methods to be used on large datasets

bioinformatics↗

bioRxiv2019

Automation for the identification of Pseudomonas aeruginosa: Comparison of TDR-300B, VITEK(R)2, and VITEK(R)-MS

Quick and precise methods have always been needed in the medical field to correctly identify the agent of infection. Automated systems for diagnosis of infectious pathogen such Pseudomonas aeruginosa from critical patients with infections in the Intensive Care Unit is essential. This study aimed to compare the capability of automated biochemistry-based identification system, TDR-300B and VITEK(R)2, to the one of Matrix-Assisted Laser Desorption/Ionization - Time of Flight (MALDI-TOF), VITEK(R)-MS, in the identification of P. aeruginosa.\n\nSamples were P. aeruginosa isolates collection from Laboratory of Microbiology, Faculty of Medicine and Health Science UNIKA Atma Jaya. These isolates were refreshed; one single colony of oxidase-positive Gram-negative rods was further inoculated in TDR-300B NF-64 cards and VITEK(R)2 GN cassettes. The bacterial identification was also carried out using VITEK(R)-MS as gold-standard. Positivity of TDR-300B and VITEK(R)2 in the identification of P. aeruginosa was 87.09% (27/31) and 90.32% (28/31) in the species level, and 87.09%, 96.77% in the genus level respectively. The congruity of TDR-300B/ VITEK(R)2 in the species and genus level was 83.87% and 87.09%. When compared to VITEK(R)-MS, congruence of VITEK(R)2 was 93.30% (24/26) and TDR-300B was 80.76% (21/26). Sensitivity value for TDR-300B and VITEK(R)2 was high, 95.45% and 100%, positive predictive value and accuracy were lower in TDR-300B than VITEK(R)2; Fisher exact value was >0.05, thus there were no significant differences in the capability of TDR-300B and VITEK(R)2 in the identification of P. aeruginosa.

microbiology↗

bioRxiv2019

Signal integration and information transfer in an allosterically regulated network

A biological reaction network may serve multiple purposes, processing more than one input and impacting downstream processes via more than one output. These networks operate in a dynamic cellular environment in which the levels of network components may change within cells and across cells. Recent evidence suggests that protein concentration variability could explain cell fate decisions. However, systems with multiple inputs, multiple outputs, and changing input concentrations have not been studied in detail due to their complexity. Here, we take a systems biochemistry approach, combining physiochemical modeling and information theory, to investigate how cyclooxygenase-2 (COX-2) processes simultaneous input signals within a complex interaction network. We find that changes in input levels affect the amount of information transmitted by the network, as does the correlation between those inputs. This, and the allosteric regulation of COX-2 by its substrates, allows it to act as a signal integrator that is most sensitive to changes in relative input levels.

systems biology↗

bioRxiv2019

LuxUS: Detecting differential DNA methylation using generalized linear mixed model with spatial correlation structure

AO_SCPLOWBSTRACTC_SCPLOWO_ST_ABSMotivationC_ST_ABSDNA methylation is an important epigenetic modification, which has multiple functions. DNA methylation and its connections to diseases have been extensively studied in recent years. It is known that DNA methylation levels of neighboring cytosines are correlated and that differential DNA methylation typically occurs rather as regions instead of individual cytosine level. ResultsWe have developed a generalized linear mixed model, LuxUS, that makes use of the correlation between neighboring cytosines to facilitate analysis of differential methylation. LuxUS implements a likelihood model for bisulfite sequencing data that accounts for experimental variation in underlying biochemistry. LuxUS can model both binary and continuous covariates, and mixed model formulation enables including replicate and cytosine random effects. Spatial correlation is included to the model through a cytosine random effect correlation structure. We show with simulation experiments that by utilizing the spatial correlation we gain more power to the statistical testing of differential DNA methylation. Results with real bisulfite sequencing data set show that LuxUS is able to detect biologically significant differentially methylated cytosines. AvailabilityThe tool is available at https://github.com/hallav/LuxUS. Supplementary informationSupplementary data are available at bioRxiv.

bioinformatics↗

bioRxiv2019

A real-time PCR for the differentiation of typhoidal and non-typhoidal Salmonella

Rapid and accurate differentiation of Salmonella spp. causing enteric fever from non-typhoidal Salmonella is essential for clinical management of cases, laboratory risk management and implementation of public health measures. Current methods used for confirmation of identification including biochemistry and serotyping as well as whole genome sequencing analyses, takes several days. Here we report the development and evaluation of a real-time PCR assay that can be performed directly on crude DNA extracts from bacterial colonies, for the rapid identification of typhoidal and non-typhoidal Salmonella. This novel two-hour assay identifies the genus Salmonella by detecting the ttr gene, encoding tetrathionate reductase, and defines typhoidal Salmonella by the detection of S. Typhi and Paratyphi-specific gene combinations. PCR assay performance was determined using 211 clinical cultures of Salmonella (114 non-typhoidal and 97 Typhoidal strains) and 7 clinical non-Salmonella cultures. In addition, the specificity of the assay was evaluated in silico using a diverse in-house collection of 1882 Salmonella whole genome sequences. The real-time PCR results for 218 isolates and the genomic analysis of the 1882 isolates produced 100% sensitivity and 100% specificity (based on a 7 gene profile) for identifying typhoidal Salmonella compared to the Salmonella whole genome sequening identification methods currently used at Public Health England. This paper describes a robust real-time PCR assay for the rapid, accurate identification of typhoidal and non-typhoidal Salmonella which will be invaluable for the urgent screening of isolates from symptomatic individuals, the safe processing of isolates in laboratories and for assisting the management of public health risks.

microbiology↗

bioRxiv2019

Biochemical characterization of the methylmercaptopropionate:cob(I)alamin methyltransferase from Methanosarcina acetivorans

Methanogenesis from methylated substrates is initiated by substrate specific methyltransferases that generate the central metabolic intermediate methyl-coenzyme M. This reaction involves a methyl-corrinoid protein intermediate and one or two cognate methyltransferases. Based on genetic data, the Methanosarcina acetivorans MtpC (corrinoid protein) and MtpA (methyltransferase) proteins were suggested to catalyze the methylmercaptopropionate(MMPA):Coenzyme M (CoM) methyl transfer reaction without a second methyltransferase. To test this, MtpA was purified after overexpression in its native host and characterized biochemically. MtpA catalyzes a robust methyl transfer reaction using free methylcob(III)alamin as the donor and mercaptopropionate (MPA) as the acceptor, with kcat of 0.315 s-1 and apparent Km for MPA of 12 M. CoM did not serve as a methyl acceptor, thus a second, unidentified methyltransferase is required to catalyze the full MMPA:CoM methyl transfer reaction. The physiologically relevant methylation of cob(I)alamin with MMPA, which is thermodynamically unfavorable, could also be demonstrated, but only at high substrate concentrations. Methylation of cob(I)alamin with methanol, dimethylsulfide, dimethylamine and methyl-CoM was not observed, even at high substrate concentrations. Although the corrinoid protein MtpC was poorly expressed alone, a stable MtpA/MtpC complex was obtained when both proteins were co-expressed. Biochemical characterization of this complex was not feasible because the corrinoid cofactor of this complex was in the inactive Co(II) state and could not be reactivated by incubation with strong reductants. The MtsF protein, comprised of both corrinoid and methyltransferase domains, co-purifies with the MtpA/MtpC, suggesting that it may be involved in MMPA metabolism. IMPORTANCEMMPA is an environmentally significant molecule produced by degradation of the abundant marine metabolite dimethylsulfoniopropionate, which plays a significant role in the biogeochemical cycles of both carbon and sulfur, with ramifications for ecosystem productivity and climate homeostasis. Detailed knowledge of the mechanisms for MMPA production and consumption is key to understanding steady state levels of this compound in the biosphere. Unfortunately, the biochemistry required for MMPA catabolism under anoxic conditions is poorly characterized. The data reported here validate the suggestion that the MtpA protein catalyzes the first step in methanogenic catabolism of MMPA. However, the enzyme does not catalyze a proposed second step required to produce the key intermediate methyl-CoM. Therefore, additional enzymes required for methanogenic MMPA catabolism await discovery.

microbiology↗

bioRxiv2019

A knowledge-based scoring function to assess the stability of quaternary protein assemblies

MotivationElucidation of protein-protein interactions is a necessary step towards understanding the complete repertoire of cellular biochemistry. Given the enormity of the problem, the expenses and limitations of experimental methods, it is imperative that this problem is tackled computationally. In silico predictions of protein interactions entail sampling different conformations of the purported complex and then scoring these to assess for interaction viability. In this study we have devised a new scheme for scoring protein-protein interactions. ResultsOur method, PIZSA (Protein Interaction Z Score Assessment) is a binary classification scheme for identification of stable protein quaternary assemblies (binders/non-binders) based on statistical potentials. The scoring scheme incorporates residue-residue contact preference on the interface with per residue-pair atomic contributions and accounts for clashes. PIZSA can accurately discriminate between native and non-native structural conformations from protein docking experiments and outperform other recently published scoring functions, demonstrated through testing on a benchmark set and the CAPRI Score_set. Though not explicitly trained for this purpose, PIZSA potentials can identify spurious interactions that are artefacts of the crystallization process. AvailabilityPIZSA is implemented as awebserverat http://cospi.iiserpune.ac.in/pizsa/. Contactmadhusudhan@iiserpune.ac.in

bioinformatics↗

bioRxiv2019

Isolating the key factors defining the magnitude of hippocampal neurogenesis' effects on anxiety, memory and pattern separation

In this paper, I analyze the hypothesis that hippocampal neurogenesis (HN) exerts its effects on behavior via activation of inhibitory circuits in the hippocampus. Using a very simple mathematical model (half-borrowed from biochemistry) to aid the reasoning, I show that the key factors determining the magnitude of HNs effects on behavior are: the baseline levels of HN in the animal, the efficiency of the animals inhibitory circuits, the strength/intensity of the stimulus presented to the animal and how much accuracy the behavioral task requires from the information contained in the hippocampal representations. Taken together, those factors can help explain patterns observed in the behavioral results for memory, pattern separation and anxiety. The conclusions of the analysis suggest that HNs effects on inhibitory circuits can explain the impact of neurogenesis in both emotion and cognition and provide a framework to interpret future studies with rodents and possibly with other species as well.

neuroscience↗

bioRxiv2019

Notch signaling and taxis mechanims regulate early stage angiogenesis: A mathematical and computational model

During angiogenesis, new blood vessels sprout and grow from existing ones. This process plays a crucial role in organ development and repair, in wound healing and in numerous pathological processes such as cancer progression or diabetes. Here, we present a mathematical model of early stage angiogenesis that permits exploration of the relative importance of mechanical, chemical and cellular cues. Endothelial cells proliferate and move over an extracellular matrix by following external gradients of Vessel Endothelial Growth Factor, adhesion and stiffness, which are incorporated to a Cellular Potts model with a finite element description of elasticity. The dynamics of Notch signaling involving Delta-4 and Jagged-1 ligands determines tip cell selection and vessel branching. Through their production rates, competing Jagged-Notch and Delta-Notch dynamics determine the influence of lateral inhibition and lateral induction on the selection of cellular phenotypes, branching of blood vessels, anastomosis (fusion of blood vessels) and angiogenesis velocity. Anastomosis may be favored or impeded depending on the mechanical configuration of strain vectors in the ECM near tip cells. Numerical simulations demonstrate that increasing Jagged production results in pathological vasculatures with thinner and more abundant vessels, which can be compensated by augmenting the production of Delta ligands. Author SummaryAngiogenesis is the process by which new blood vessels grow from existing ones. This process plays a crucial role in organ development, in wound healing and in numerous pathological processes such as cancer growth or in diabetes. Angiogenesis is a complex, multi-step and well regulated process where biochemistry and physics are intertwined. The process entails signaling in vessel cells being driven by both chemical and mechanical mechanisms that result in vascular cell movement, deformation and proliferation. Mathematical models have the ability to bring together these mechanisms in order to explore their relative relevance in vessel growth. Here, we present a mathematical model of early stage angiogenesis that is able to explore the role of biochemical signaling and tissue mechanics. We use this model to unravel the regulating role of Jagged, Notch and Delta dynamics in vascular cells. These membrane proteins have an important part in determining the leading cell in each neo-vascular sprout. Numerical simulations demonstrate that increasing Jagged production results in pathological vasculatures with thinner and more abundant vessels, which can be compensated by augmenting the production of Delta ligands.

systems biology↗

bioRxiv2019

Transverse sinus injections: A novel method for whole-brain vector-driven gene delivery

A major challenge in neuroscience is convenient whole-brain delivery of transgenes, especially early in postnatal development. Recent advances demonstrate whole-brain gene delivery by retro-orbital injection of virus, but slow and sparse expression, and the large injection volumes required, precludes early developmental studies. We developed a novel method for simple, fast and efficient gene delivery across the central nervous system in neonates as early as P4 and persisting into adulthood. The method employs transverse sinus injections of 2-4L of AAV9 at P0. Here, we describe how to use this method to label and/or genetically manipulate cells in the neonatal rat and mouse brain. This protocol is fast, easy, can be readily adopted by any laboratory, and utilizes the widely available AAV9 capsid. The procedure outlined here is adaptable for diverse experimental applications ranging from biochemistry, anatomical and functional mapping, to gene expression, silencing, and editing.

neuroscience↗

bioRxiv2019

The C-terminal domains SnRK2-box and ABA-box have a role in sugarcane SnRK2s auto-activation and activity

Resistance to drought stress is fundamental to plant survival and development. Abscisic acid (ABA) is one of the major hormones involved in different types of abiotic and biotic stress responses. ABA intracellular signaling has been extensively explored in Arabidopsis thaliana and occurs via a phosphorylation cascade mediated by three related protein kinases, denominated SnRK2s (SNF1-related protein kinases). However, the role of ABA signaling and the biochemistry of SnRK2 in crop plants remains underexplored. Considering the importance of the ABA hormone in abiotic stress tolerance, here we investigated the regulatory mechanism of sugarcane SnRK2s - known as SAPKs (Stress/ABA-activated Protein Kinases). The crystal structure of ScSAPK10 revealed the characteristic SnRK2 family architecture, in which the regulatory SnRK2-box interacts with the kinase domain C helix. To study sugarcane SnRK2 regulation, we produced a series of mutants for the protein regulatory domains SnRK2-box and ABA-box. Surprisingly, mutations in the SnRK2-box did not drastically affect sugarcane SnRK2 activity, in contrast to previous observations for the homologous proteins in Arabidopsis. Also, we found that the ABA-box might have a role in SnRK2 activation in the absence of PP2C phosphatase. Taken together, our results demonstrate that both C-terminal domains of sugarcane SnRK2 proteins play a fundamental role in protein activation and activity.

plant biology↗

bioRxiv2019

Control Theory Concepts for Modeling Uncertainty in Enzyme Kinetics of Biochemical Networks

Analysis of the dynamic and steady-state properties of biochemical networks hinge on information about the parameters of enzyme kinetics. The lack of experimental data characterizing enzyme activities and kinetics along with the associated uncertainties impede the development of kinetic models, and researchers commonly use Monte Carlo sampling to explore the parameter space. However, the sampling of parameter spaces is a computationally expensive task for larger biochemical networks. To address this issue, we exploit the fact that reaction rates of biochemical reactions and network responses can be expressed as a function of displacements from thermodynamic equilibrium of elementary reaction steps and concentrations of free enzymes and their intermediary complexes. For a set of kinetic mechanisms ubiquitously found in biochemistry, we express kinetic responses of enzymes to changes in network metabolite concentrations through these quantities both analytically and schematically. The tailor-made sampling of these quantities allows for characterizing the missing kinetic parameters and accelerating the efforts towards building genome-scale kinetic metabolic models.

systems biology↗

bioRxiv2019

Isolated occurrences of membrane perturbation by mechanosensing from weakly aggregating silver nanoparticles

Silver nanoparticles (AgNPs) have wide-ranging applications, including as additives in consumer products and in medical diagnostics and therapy. Therefore understanding how AgNPs interact with biological systems is important for ascertaining any potential health risks due to the likelihood of high levels of human exposure. Besides any severe, acute effects, it is desirable to understand more subtle interactions that could lead to milder, chronic health impacts. Nanoparticles are small enough to be able to enter biological cells and interfere with their internal biochemistry. The initial contact between nanoparticle and cell is at the plasma membrane. To gain fundamental mechanistic insight into AgNP-membrane interactions, we investigate these phenomena in minimal model systems using a wide-range of biophysical approaches applied to lipid vesicles. We find a strong dependence on the medium composition, where colloidally stable AgNPs in a glucose buffer have negligible effect on the membrane. However, at a physiological salt concentrations, the AgNPs start to weakly aggregate and sporadic but significant membrane perturbation events are observed. Under these latter conditions, transient poration and structural remodelling of some vesicle membranes is observed. We observe that the fluidity of giant vesicle membranes universally decreases by an average of 16% across all vesicles. However, we observe a small population of vesicles display a significant change in mechanical properties with lower bending rigidity and higher membrane tension. Therefore we argue that the isolated occurrences of membrane perturbation by AgNPs are due to low probability mechanosensing events of AgNP aggregation at the membrane.\n\nGRAPHICAL ABSTRACT\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=109 SRC=\"FIGDIR/small/623678v1_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (54K):\norg.highwire.dtl.DTLVardef@196aa51org.highwire.dtl.DTLVardef@87c12borg.highwire.dtl.DTLVardef@9b2096org.highwire.dtl.DTLVardef@7877f6_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗

bioRxiv2019

High-content assay for precision medicine discovery in cystic fibrosis.

CFTR, a plasma membrane anion channel, plays a key role in controlling transepithelial fluid movement. Excessive activation results in intestinal fluid loss during secretory diarrhoeas, while CFTR mutations underlie cystic fibrosis (CF). Anion permeability depends both on how well CFTR channels work (permeation/gating) and on how many are present at the membrane (reflecting folding, trafficking, metabolic stability). Recently, treatments with two drug classes targeting CFTR - one boosting ion-channel function (potentiators), the other increasing plasma membrane density (correctors) - have provided significant health benefits to CF patients. Here we present an image-based fluorescence assay that can rapidly and simultaneously estimate both CFTR ion-channel function and the proteins proximity to the membrane. We monitor F508del-CFTR, the most common CF-causing variant, and confirm rescue by low temperature, CFTR-targeting drugs and second-site revertant mutation R1070W. In addition, we characterize a panel of 62 CF-causing mutations. Our measurements correlate well with published data (electrophysiology and biochemistry), further confirming validity of the assay. Finally, we profile effects of acute treatment with approved potentiator drug VX-770 on the rare-mutation panel. Mapping the potentiation profile on CFTR structures raises mechanistic hypotheses on drug action, suggesting that VX-770 might allow an open-channel conformation with an alternative arrangement of domain interfaces around site 1. The assay is a valuable tool for investigation of CFTR molecular mechanisms, allowing accurate inferences on gating/permeation. In addition, by providing a two-dimensional characterization of the CFTR protein, it could better inform development of single-drug and precision therapies addressing the root cause of CF disease.

pharmacology and toxicology↗

bioRxiv2019

Generating kinetic environments to study dynamic cellular processes in single cells

Cells of any organism are consistently exposed to changes over time in their environment. The kinetics by which these changes occur are critical for the cellular response and fate decision. It is therefore important to control the temporal changes of extracellular stimuli precisely to understand biological mechanisms in a quantitative manner. Most current cell culture and biochemical studies focus on instant changes in the environment and therefore neglect the importance of kinetic environments. To address these shortcomings, we developed two experimental methodologies to precisely control the environment of single cells. These methodologies are compatible with standard biochemistry, molecular, cell and quantitative biology assays. We demonstrate applicability by obtaining time series and time point measurements in both live and fixed cells. We demonstrate the feasibility of the methodology in yeast and mammalian cell culture in combination with widely used assays such as flow cytometry, time-lapse microscopy and single-molecule RNA Fluorescent in-situ Hybridization. Our experimental methodologies are easy to implement in most laboratory settings and allows the study of kinetic environments in a wide range of assays and different cell culture conditions.

systems biology↗

bioRxiv2019

Unveiling the multi-step solubilization mechanism of single vesicles by detergents.

The solubilization of membranes by detergents is critical for many technological applications and has become widely used in biochemistry research to induce cell rupture, extract cell constituents, and to purify, reconstitute and crystallize membrane proteins. The thermodynamic details of solubilization have been extensively investigated, but the kinetic aspects remain poorly understood. Here we used a combination of single-vesicle Forster resonance energy transfer (svFRET), fluorescence correlation spectroscopy and quartz-crystal microbalance with dissipation monitoring to access the real-time kinetics and elementary solubilization steps of sub-micron sized vesicles, which are inaccessible by conventional diffraction-limited optical methods. Real-time injection of a non-ionic detergent, Triton X, induced biphasic solubilization kinetics of surface-immobilized vesicles labelled with the Dil/DiD FRET pair. The nanoscale sensitivity accessible by svFRET allowed us to unambiguously assign each kinetic step to distortions of the vesicle structure comprising an initial fast vesicle-swelling event followed by slow lipid loss and micellization. We expect the svFRET platform to be applicable beyond the sub-micron sizes studied here and become a unique tool to unravel the complex kinetics of detergent-lipid interactions.

biophysics↗